[A-CG37] Global Carbon Cycle Observation and Analysis

[ACG37-P02] Refinement of a Model for Upscaling of Soil Respiration in Japan

*Hina Yamanuki¹, Kazuhito Ichii¹, Yuhei Yamamoto¹, Shunji Kotsuki¹, Lifei Sun², Naishen Liang², Munemasa Teramoto³, Hirohiko Nagano⁴, Takashi Hirano⁵, Kentaro Takagi⁵, Sachinobu Ishida⁶, Masahiro Takagi⁷, Toshiaki Kondo⁸, Jun Koarashi⁹, Mariko Atarashi-Andoh⁹, Yoshiyuki Takahashi² (1.Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba 263-8522, Japan, 2.National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan, 3.Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, Tottori 680-0001, Japan, 4.Niigata University, 8050 Ikarashi 2-no-cho, Nishi-ku, Niigata, Niigata 950-2181, Japan, 5.Hokkaido University, Kita 8, Nishi 5, Kita-ku, Sapporo, Hokkaido 060-0808, Japan, 6.Hirosaki University, 3 Bunkyo-cho, Hirosaki, Aomori 036-8561, Japan, 7.University of Miyazaki, Gakuen-kibanadai-nishi-1-1, Miyazaki, Miyazaki 889-2192, Japan, 8.Japan International Research Center for Agricultural Sciences, 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan, 9.Japan Atomic Energy Agency, 2-4 Shirakata, Tokai-mura, Naka-gun, Ibaraki 319-1195, Japan)

Keywords:Carbon Cycle, Soil Respiration, Remote Sensing, Machine Learning, Upscaling, CO2 Flux

Soil Respiration (SR), the sum of root respiration and heterotrophic respiration, is one of the most essential components of soil carbon cycles. However, large uncertainties remain in its temporal and spatial variations. So far, various efforts have been conducted to understand SRs. Many observation stations directly measure SR using chambers. Using these observation data and literature surveys, several studies estimated spatio-temporal patterns of SR at global and regional scales based on semi-empirical models and machine-learning methods. However, the database (e.g. Soil Respiration Database; SRDB) used in these large-scale studies contains inconsistently observed datasets. These inconsistencies may produce additional uncertainties in estimated fluxes. The largest SR observation network across Asia developed and maintained by NIES, Japan can be a good candidate to estimate spatio-temporal variations in SR across Asia, since these observations have been conducted with a consistent observation protocol and quality controls.

In this study, we refined our model to estimate SR across Japan with observation data (eight sites across Japan), remote sensing data (MODIS land products), and random forest regression. We newly added soil temperature and moisture by a process-based model, the Simple Biosphere model including Urban Canopy (SiBUC). Our estimation shows a reasonable performance with R²=0.72 for the in-situ model and R²=0.73 for remote sensing and in-situ combined model on average. Based on the established model, we also produced upscaled estimations of SR across Japan with a spatial resolution of 4 km from 2000 to 2020.

Intercomparison of our estimation with other available datasets was also conducted to understand the advantages of our estimation. Our results show spatially more explicit variations compared with other global products. In addition, our advantage is to capture temporal variations (e.g. 8 days). We also confirmed that previous estimations do not reproduce our observation network datasets, indicating consistent observation approach is important to upscale soil respiration.